Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge by Shuntaro Aotake, Takuya Otani, Masatoshi Funabashi, Atsuo Takanishi

“…The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. …”
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Digital twin-enabled post-disaster damage and recovery monitoring with deep learning: leveraging transfer learning, attention mechanisms, and explainable AI by Umut Lagap, Saman Ghaffarian

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Efficient remote sensing image classification using the novel STConvNeXt convolutional network by Bo Liu, Chenmei Zhan, Cheng Guo, Xiaobo Liu, Shufen Ruan

“…Ablation studies further confirm the effectiveness of each module design, particularly demonstrating that the model maintains excellent classification accuracy despite a substantial reduction in parameters.…”
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Approach to analysis of inter-regional similarity of investment activity support measures in legislation of regions (on the example of Krasnoyarsk region) by Valentina F. Lapo

“…The proximity coefficient matrix will be especially useful in spatial econometric models. The offered approach and parameters can be applied to research the other positions of the regional legislation.…”
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Volumetric evolution of supraglacial lakes in southwestern Greenland using ICESat-2 and Sentinel-2 by T. Feng, T. Feng, X. Ma, X. Ma, X. Liu, X. Liu, X. Liu

“…The trained MLP model is then used to estimate the depth over the entire lake areas. …”
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Re-identification assistance and multi-stage association for pedestrian multi-object tracking by Ye Li, Li Zhan, Lei Wu, Hongkun Liu, Jianli Min, Xinzhong Wang

“…Currently, most methods mainly use the Kalman filter to model the motion and predict the position of the pedestrian in the next frame. …”
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The Value of Learning about Natural History in Biodiversity Markets. by Douglas J Bruggeman

“…I use an individually-based, spatially-explicit population model for the Red-cockaded Woodpecker (Picoides borealis) to predict spatial- and temporal- influences of landscape change on species occurrence and genetic diversity. …”
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FROM PIXELS TO DIAGNOSIS: A DEEP LEARNING FRAMEWORK FOR HISTOPATHOLOGICAL IMAGE ANALYSIS IN CANINE TESTICULAR PATHOLOGY

“…We fine-tuned the model on a proprietary dataset of testicular tissue sections, annotated by two pathologists. …”
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Le rôle des facteurs locaux sur les effets des changements climatiques au Quaternaire supérieur sur les environnements naturel du Néguev septentrional by Aaron Yair

“…However; existing global models are not yet capable of predicting accurately the evolution of regional climates and their possible impact on the environment. …”
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Simulation of urban development in the City of Rome: Framework, methodology, and problem solving by Simone Di Zio, Armando Montanari, Barbara Staniscia

“…In Italy’s case, the implementation of the UrbanSIM model involved the territory of Rome, including the municipalities of Rome and Fiumicino. …”
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Comparison of Deep and Machine Learning Approaches for Quebec Tree Species Classification Using a Combination of Multispectral and LiDAR Data by Omid Reisi Gahrouei, Jean-François Côté, Philippe Bournival, Philippe Giguère, Martin Béland

“…The results indicated that Dense Convolution Network (DenseNet) achieved the best overall accuracy of 78%, outperforming machine learning methods by 5%. In addition, the models’ performance was independently assessed using permanent and temporary ground sample plots, acknowledging the superiority of DenseNet in terms of overall accuracy (73%) in predicting the dominant species. …”
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Mapping the global distribution of livestock. by Timothy P Robinson, G R William Wint, Giulia Conchedda, Thomas P Van Boeckel, Valentina Ercoli, Elisa Palamara, Giuseppina Cinardi, Laura D'Aietti, Simon I Hay, Marius Gilbert

“…The Gridded Livestock of the World (GLW) database, produced in 2007, provided modelled livestock densities of the world, adjusted to match official (FAOSTAT) national estimates for the reference year 2005, at a spatial resolution of 3 minutes of arc (about 5×5 km at the equator). …”
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Spatiotemporal Dynamics and Driving Factors of Soil Salinization: A Case Study of the Yutian Oasis, Xinjiang, China by Shiqin Li, Ilyas Nurmemet, Jumeniyaz Seydehmet, Xiaobo Lv, Yilizhati Aili, Xinru Yu

“…Improvements in secondary salinization and increased vegetation coverage were the primary contributors to this reduction. Geo Detector analysis highlighted vegetation (NDVI) as the dominant factor, and its interaction with soil moisture (NDWI) has a significant impact on the spatial and temporal distribution of soil salinity. …”
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Range dynamics of Anopheles mosquitoes in Africa suggest a significant increase in the malaria transmission risk by Peixiao Nie, Chunyan He, Jianmeng Feng

“…Through a unified scheme, we developed 21 species distribution models to predict the range dynamics of 21 major An. species in Africa under future scenarios and also examined their overall range dynamic patterns mainly through suitability overlap index and range overlap index. …”
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Estimating the impact of interventions against COVID-19: From lockdown to vaccination. by James Thompson, Stephen Wattam

“…Our model predicts far fewer cases and deaths than the equivalent equation-based SEIR model. …”
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The impact of land use/land cover change on ecosystem services in Golestan province by Hamidreza Kamyab, Nasim Shabani

“…Markov Chain Analysis is a convenient tool for modeling land use change, when changes and processes in the landscape are difficult to describe. …”
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A Unified Optimization Framework for Cost-Effective and Voltage-Stable Operation of Renewable Energy-Based Microgrids Using Mixed-Integer Nonlinear Programming by Peter Anuoluwapo Gbadega, Yanxia Sun, Olufunke Abolaji Balogun

“…The OPF analysis focuses on the spatial and temporal distribution of active and reactive power within the three-bus grid-tied microgrid while ensuring voltage stability within a ±10% boundary of the nominal 6kV. …”
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High accuracy indoor positioning system using Galois field-based cryptography and hybrid deep learning by Mohammad Mazyad Hazzazi, Prashant Kumar Shukla, Piyush Kumar Shukla, Fahad Alblehai, Sameer Nooh, Mohd Asif Shah

“…The online phase involves extracting information to improve the model’s accuracy. These features can be signal-based, spatial–temporal, motion-based, or environmental. …”
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Assessing climate risks from satellite imagery with machine learning: A case study of flood risks in Jakarta by Jeasurk Yang, Donghyun Ahn, Junbeom Bahk, Sungwon Park, Nurrokhmah Rizqihandari, Meeyoung Cha

“…Our research demonstrates that disaster-related risk measurement becomes more comprehensive and multi-faceted by including the following components: hazards, exposure, and vulnerability. Our model first maps hazard-related risks with geo-spatial data, then extends the model to incorporate exposure and vulnerability. …”
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Exponential Improvements in the Simulation of Lattice Gauge Theories Using Near-Optimal Techniques by Mason L. Rhodes, Michael Kreshchuk, Shivesh Pathak

“…We find a reduction of up to 25 orders of magnitude in space-time volume over Trotter methods for simulations of non-Abelian lattice gauge theories relevant to the standard model. …”
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